Prompt with me: Building a ReAct self-coding AI agent from scratch - no LangChain or code!
TLDRIn this tutorial, the presenter guides viewers through building a simple ReAct self-coding AI agent in Wordware, designed to solve math problems. The agent operates on a thought-action-input-observation loop, utilizing tools like a JavaScript code interpreter and Google search. The process involves defining actions, inputs, and observations within a loop, iterating up to 10 times to refine the solution. The example demonstrates solving a math power operation, highlighting the agent's ability to autonomously decide and execute tasks.
Takeaways
- 😀 The tutorial aims to build a simple self-coding AI agent in 10 minutes without using LangChain or code.
- 🤖 An agent is defined as an AI-powered entity, typically driven by language models, capable of autonomous decision-making.
- 🔄 The agent operates on a 'react' model, which includes a thought-action-input-observation loop to solve tasks.
- 🧠 The agent is designed to solve math problems using specified tools, such as a JavaScript code interpreter and Google search.
- 🛠️ The 'run code' tool executes JavaScript code and returns the output, while the 'Google search' tool performs internet searches based on input queries.
- 📝 The agent follows a structured format for problem-solving, which includes defining a question, thought, action, input, observation, and final answer.
- 🔢 The example given is to calculate (99,999) to the power of 5 using the 'run code' tool.
- 🔍 The agent includes a loop mechanism, referred to as the 'react loop,' with a maximum of 10 iterations to find a solution.
- 🚫 The agent must ensure that actions are valid, distinguishing between 'run code' and 'Google search' and handling errors appropriately.
- 🔚 The final output is a concise final answer, which the agent determines after completing the thought-action cycle.
- 🔄 The process involves debugging and refining the agent's actions, such as fixing syntax errors in code and adjusting input formats.
Q & A
What is the main purpose of the 'React agent' discussed in the transcript?
-The main purpose of the 'React agent' is to autonomously decide what to do and execute actions to solve tasks, particularly math problems in this context, by utilizing tools and language models.
What are the core components of a React agent's thought-action-input-observation loop?
-The core components of a React agent's loop are thinking about what to do next, deciding on an action from a set of allowed actions, executing the action, and observing the result of the action.
How does the agent utilize tools to solve math problems?
-The agent uses tools such as a JavaScript code interpreter to execute mathematical operations and a Google search function to find information, which aids in solving math problems.
What is the structure that the agent should follow according to the transcript?
-The agent should follow a structure that includes a question, a thought about what to do, an action to take (either 'Google search' or 'run code'), the input for the action, and the observation which is the result of the action.
What is the maximum number of iterations set for the React Loop in the script?
-The maximum number of iterations set for the React Loop is 10.
How does the agent decide whether to run code or perform a Google search?
-The agent decides based on the thought process and the nature of the task. If it requires executing a mathematical operation, it will run code; if it requires information gathering, it will perform a Google search.
What is the role of the observation logs in the agent's process?
-Observation logs record the results of the actions taken by the agent, which helps in understanding the outcome of each step and informs the subsequent decisions.
How does the agent ensure that the input for the 'run code' action is valid JavaScript code?
-The agent checks if the input is a valid JavaScript code by using a prompt that specifically handles code execution and ensures that the input is formatted correctly.
What happens if the agent chooses an invalid action?
-If the agent chooses an invalid action, it will be informed that the action is not valid and should only be 'Google search' or 'run code'.
What is the final output of the agent after going through the thought-action-input-observation loop?
-The final output is the solution to the math problem, which the agent arrives at after iterating through the loop and making decisions based on observations.
How does the agent handle errors or unexpected results during the process?
-The agent handles errors by iterating through the loop, making adjustments based on observations, and ensuring that the final answer is correct and valid.
Outlines
🤖 Building a Simple AI Agent for Math Problems
The script introduces the process of creating a simple AI agent within Wordware, aimed at solving mathematical problems. The agent is described as a 'react agent' that operates on a thought, action, and observation loop. It uses language models for decision-making and can utilize tools like a JavaScript code interpreter and Google search. The agent is designed to think about its next move, choose an action from a set of allowed actions, execute it, and observe the result. The script outlines the structure for the agent's operation and emphasizes the importance of using the right tools and format for problem-solving.
🔁 The React Loop for Iterative Problem Solving
This paragraph delves into the specifics of the 'react loop', which is a core component of the AI agent's operation. The loop is set to iterate up to 10 times, allowing the agent to process user input, think, decide on an action, and execute it. Actions can include running code or performing a Google search. The paragraph emphasizes the importance of input validation and the need for the agent to understand and differentiate between running code and performing a search. It also discusses the need for the agent to output clear logs and to stop the action loop when it starts outputting observations.
🛠️ Refining the Agent's Code Execution and Search Process
The final paragraph discusses the refinement of the agent's code execution and search process. It highlights the need to ensure that the agent correctly formats its input, particularly when dealing with mathematical operations. The paragraph also addresses a specific issue encountered during testing, where the agent incorrectly added asterisks, leading to an illegal return statement. The solution involves adjusting the input format to prevent such errors. The agent's ability to loop and decide when to stop is also emphasized, with the final output being the agent's final answer after considering all iterations.
Mindmap
Keywords
💡ReAct self-coding AI agent
💡LangChain
💡Wordware
💡Agent
💡Thought, Action, Input, and Observation Loop
💡Math problem-solving agent
💡JavaScript code interpreter
💡Google search
💡Rules
💡React Loop
💡Final answer
Highlights
Introduction to building a simple AI agent in Wordware without using LangChain or code.
Definition of an agent as an AI-powered, autonomous decision-making entity.
Explanation of a React agent with a thought, action, input, and observation loop.
The agent's purpose to solve math problems using specified tools.
Description of the first tool: a JavaScript code interpreter for executing math operations.
Capability of the agent to search Google for information.
Introduction of the 'rules' structure for the agent's decision-making process.
Format for the agent's thought process and actions based on the paper's guidelines.
The React Loop with a maximum of 10 iterations for problem-solving.
Process of deciding on an action: Google search or running code.
Implementation of a prompt to run JavaScript code.
Utilization of another prompt for performing Google searches.
Handling invalid actions with an error statement.
Final output of the agent's answer after the thought and action process.
Example of the agent solving a math problem with JavaScript code.
Identification of issues with input formatting and the need for adjustments.
Iteration and refinement of the agent's process to improve accuracy.
Finalization of the agent's response after 10 loops with the correct answer.
